Optimization
- Continuous model parameters
- Meta or tuning parameters
- Combinatorial structures, e.g. models
Simulation and Integration
- Mean and variance, \(E f(X) = \int f(X) \ dP\)
- Probabilities, \(P(X \in A) = \int_{(X \in A)} \ dP\)
- Marginalization, Bayesian posteriors